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  <span class="target" id="module-optuna.multi_objective.samplers"></span><div class="section" id="samplers">
<h1>Samplers<a class="headerlink" href="#samplers" title="永久链接至标题">¶</a></h1>
<dl class="py class">
<dt id="optuna.multi_objective.samplers.BaseMultiObjectiveSampler">
<em class="property">class </em><code class="sig-prename descclassname">optuna.multi_objective.samplers.</code><code class="sig-name descname">BaseMultiObjectiveSampler</code><a class="reference internal" href="../../_modules/optuna/multi_objective/samplers/_base.html#BaseMultiObjectiveSampler"><span class="viewcode-link">[源代码]</span></a><a class="headerlink" href="#optuna.multi_objective.samplers.BaseMultiObjectiveSampler" title="永久链接至目标">¶</a></dt>
<dd><p>multi-objective sampler 基类。</p>
<p>该类的抽象方法和 <a class="reference internal" href="../samplers.html#optuna.samplers.BaseSampler" title="optuna.samplers.BaseSampler"><code class="xref py py-class docutils literal notranslate"><span class="pre">BaseSampler</span></code></a> 定义的抽象方法一致，除了接受 multi-objective 版 study 和 trial 实例作为参数之外。</p>
<div class="admonition note">
<p class="admonition-title">注解</p>
<p>在 v1.4.0 中作为试验性特性引入，在未来版本中，该接口可能在没有预先告知的情况下被改变。参考 <a class="reference external" href="https://github.com/optuna/optuna/releases/tag/v1.4.0">https://github.com/optuna/optuna/releases/tag/v1.4.0</a>.</p>
</div>
<dl class="py method">
<dt id="optuna.multi_objective.samplers.BaseMultiObjectiveSampler.infer_relative_search_space">
<em class="property">abstract </em><code class="sig-name descname">infer_relative_search_space</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">study</span><span class="p">:</span> <span class="n"><a class="reference internal" href="study.html#optuna.multi_objective.study.MultiObjectiveStudy" title="optuna.multi_objective.study.MultiObjectiveStudy">optuna.multi_objective.study.MultiObjectiveStudy</a></span></em>, <em class="sig-param"><span class="n">trial</span><span class="p">:</span> <span class="n"><a class="reference internal" href="trial.html#optuna.multi_objective.trial.FrozenMultiObjectiveTrial" title="optuna.multi_objective.trial.FrozenMultiObjectiveTrial">optuna.multi_objective.trial.FrozenMultiObjectiveTrial</a></span></em><span class="sig-paren">)</span> &#x2192; Dict<span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(在 Python v3.8)">str</a><span class="p">, </span>optuna.distributions.BaseDistribution<span class="p">]</span><a class="reference internal" href="../../_modules/optuna/multi_objective/samplers/_base.html#BaseMultiObjectiveSampler.infer_relative_search_space"><span class="viewcode-link">[源代码]</span></a><a class="headerlink" href="#optuna.multi_objective.samplers.BaseMultiObjectiveSampler.infer_relative_search_space" title="永久链接至目标">¶</a></dt>
<dd><p>推断将在 target trial 中使用的搜索空间。</p>
<p>该方法在 <a class="reference internal" href="#optuna.multi_objective.samplers.BaseMultiObjectiveSampler.sample_relative" title="optuna.multi_objective.samplers.BaseMultiObjectiveSampler.sample_relative"><code class="xref py py-func docutils literal notranslate"><span class="pre">sample_relative()</span></code></a> 方法之前被调用。而该方法返回的搜索空间会被传入它。那些不包含在该搜索空间中的参数将通过 <a class="reference internal" href="#optuna.multi_objective.samplers.BaseMultiObjectiveSampler.sample_independent" title="optuna.multi_objective.samplers.BaseMultiObjectiveSampler.sample_independent"><code class="xref py py-func docutils literal notranslate"><span class="pre">sample_independent()</span></code></a> 来采样。</p>
<dl class="field-list simple">
<dt class="field-odd">参数</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>study</strong> -- Target study object.</p></li>
<li><p><strong>trial</strong> -- Target trial object.</p></li>
</ul>
</dd>
<dt class="field-even">返回</dt>
<dd class="field-even"><p>一个包含了参数名和参数分布的字典。</p>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">参见</p>
<p>参见 作为 <a class="reference internal" href="#optuna.multi_objective.samplers.BaseMultiObjectiveSampler.infer_relative_search_space" title="optuna.multi_objective.samplers.BaseMultiObjectiveSampler.infer_relative_search_space"><code class="xref py py-func docutils literal notranslate"><span class="pre">infer_relative_search_space()</span></code></a> 的实现的 <a class="reference internal" href="../samplers.html#optuna.samplers.intersection_search_space" title="optuna.samplers.intersection_search_space"><code class="xref py py-func docutils literal notranslate"><span class="pre">intersection_search_space()</span></code></a>。</p>
</div>
</dd></dl>

<dl class="py method">
<dt id="optuna.multi_objective.samplers.BaseMultiObjectiveSampler.sample_independent">
<em class="property">abstract </em><code class="sig-name descname">sample_independent</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">study</span><span class="p">:</span> <span class="n"><a class="reference internal" href="study.html#optuna.multi_objective.study.MultiObjectiveStudy" title="optuna.multi_objective.study.MultiObjectiveStudy">optuna.multi_objective.study.MultiObjectiveStudy</a></span></em>, <em class="sig-param"><span class="n">trial</span><span class="p">:</span> <span class="n"><a class="reference internal" href="trial.html#optuna.multi_objective.trial.FrozenMultiObjectiveTrial" title="optuna.multi_objective.trial.FrozenMultiObjectiveTrial">optuna.multi_objective.trial.FrozenMultiObjectiveTrial</a></span></em>, <em class="sig-param"><span class="n">param_name</span><span class="p">:</span> <span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(在 Python v3.8)">str</a></span></em>, <em class="sig-param"><span class="n">param_distribution</span><span class="p">:</span> <span class="n">optuna.distributions.BaseDistribution</span></em><span class="sig-paren">)</span> &#x2192; Any<a class="reference internal" href="../../_modules/optuna/multi_objective/samplers/_base.html#BaseMultiObjectiveSampler.sample_independent"><span class="viewcode-link">[源代码]</span></a><a class="headerlink" href="#optuna.multi_objective.samplers.BaseMultiObjectiveSampler.sample_independent" title="永久链接至目标">¶</a></dt>
<dd><p>对给定的分布进行参数采样。</p>
<p>该方法只在参数不包含在 <code class="xref py py-func docutils literal notranslate"><span class="pre">sample_relative()</span></code> 方法返回的结果中的情况下使用。该方法适用于那些不依赖参数之间关系的采样算法，比如随机采样。</p>
<dl class="field-list simple">
<dt class="field-odd">参数</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>study</strong> -- Target study object.</p></li>
<li><p><strong>trial</strong> -- Target trial object.</p></li>
<li><p><strong>param_name</strong> -- 采样参数名</p></li>
<li><p><strong>param_distribution</strong> -- 指定了先验或者/和采样算法的分布对象。</p></li>
</ul>
</dd>
<dt class="field-even">返回</dt>
<dd class="field-even"><p>参数值。</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt id="optuna.multi_objective.samplers.BaseMultiObjectiveSampler.sample_relative">
<em class="property">abstract </em><code class="sig-name descname">sample_relative</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">study</span><span class="p">:</span> <span class="n"><a class="reference internal" href="study.html#optuna.multi_objective.study.MultiObjectiveStudy" title="optuna.multi_objective.study.MultiObjectiveStudy">optuna.multi_objective.study.MultiObjectiveStudy</a></span></em>, <em class="sig-param"><span class="n">trial</span><span class="p">:</span> <span class="n"><a class="reference internal" href="trial.html#optuna.multi_objective.trial.FrozenMultiObjectiveTrial" title="optuna.multi_objective.trial.FrozenMultiObjectiveTrial">optuna.multi_objective.trial.FrozenMultiObjectiveTrial</a></span></em>, <em class="sig-param"><span class="n">search_space</span><span class="p">:</span> <span class="n">Dict<span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(在 Python v3.8)">str</a><span class="p">, </span>optuna.distributions.BaseDistribution<span class="p">]</span></span></em><span class="sig-paren">)</span> &#x2192; Dict<span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(在 Python v3.8)">str</a><span class="p">, </span>Any<span class="p">]</span><a class="reference internal" href="../../_modules/optuna/multi_objective/samplers/_base.html#BaseMultiObjectiveSampler.sample_relative"><span class="viewcode-link">[源代码]</span></a><a class="headerlink" href="#optuna.multi_objective.samplers.BaseMultiObjectiveSampler.sample_relative" title="永久链接至目标">¶</a></dt>
<dd><p>在给定的搜索空间中采样。</p>
<p>在每个 trial 开始时该方法会被调用一次，也就是在目标函数被求值前被调用。该方法适用于那些依赖参数间关系的采样算法。</p>
<dl class="field-list simple">
<dt class="field-odd">参数</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>study</strong> -- Target study object.</p></li>
<li><p><strong>trial</strong> -- Target trial object.</p></li>
<li><p><strong>search_space</strong> -- 由 <a class="reference internal" href="#optuna.multi_objective.samplers.BaseMultiObjectiveSampler.infer_relative_search_space" title="optuna.multi_objective.samplers.BaseMultiObjectiveSampler.infer_relative_search_space"><code class="xref py py-func docutils literal notranslate"><span class="pre">infer_relative_search_space()</span></code></a> 返回的搜索空间。</p></li>
</ul>
</dd>
<dt class="field-even">返回</dt>
<dd class="field-even"><p>包含了参数名和参数值的字典。</p>
</dd>
</dl>
</dd></dl>

</dd></dl>

<dl class="py class">
<dt id="optuna.multi_objective.samplers.NSGAIIMultiObjectiveSampler">
<em class="property">class </em><code class="sig-prename descclassname">optuna.multi_objective.samplers.</code><code class="sig-name descname">NSGAIIMultiObjectiveSampler</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">population_size</span><span class="p">:</span> <span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(在 Python v3.8)">int</a></span> <span class="o">=</span> <span class="default_value">50</span></em>, <em class="sig-param"><span class="n">mutation_prob</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(在 Python v3.8)">float</a><span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">crossover_prob</span><span class="p">:</span> <span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(在 Python v3.8)">float</a></span> <span class="o">=</span> <span class="default_value">0.9</span></em>, <em class="sig-param"><span class="n">swapping_prob</span><span class="p">:</span> <span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(在 Python v3.8)">float</a></span> <span class="o">=</span> <span class="default_value">0.5</span></em>, <em class="sig-param"><span class="n">seed</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(在 Python v3.8)">int</a><span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/optuna/multi_objective/samplers/_nsga2.html#NSGAIIMultiObjectiveSampler"><span class="viewcode-link">[源代码]</span></a><a class="headerlink" href="#optuna.multi_objective.samplers.NSGAIIMultiObjectiveSampler" title="永久链接至目标">¶</a></dt>
<dd><p>使用 NSGA-II 算法的 Multi-objective sampler。</p>
<p>NSGA-II 指 &quot;Nondominated Sorting Genetic Algorithm II&quot;，这是一个著名的快速且优秀 multi-objective 遗传算法。</p>
<p>要了解更多关于 NSGA-II 的信息的话，请参考以下论文：</p>
<ul class="simple">
<li><p><a class="reference external" href="https://ieeexplore.ieee.org/document/996017">A fast and elitist multiobjective genetic algorithm: NSGA-II</a></p></li>
</ul>
<dl class="field-list simple">
<dt class="field-odd">参数</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>population_size</strong> -- 每一代中的 个体（trial）数。</p></li>
<li><p><strong>mutation_prob</strong> -- 在创建新个体时每个参数突变的概率。如果设置成 <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(在 Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a> 的话，那么该值实际上会是 <code class="docutils literal notranslate"><span class="pre">1.0</span> <span class="pre">/</span> <span class="pre">len(parent_trial.params)</span></code> 。其中 <code class="docutils literal notranslate"><span class="pre">parent_trial</span></code> 是 target trial 的父代 trial。</p></li>
<li><p><strong>crossover_prob</strong> -- 产生新的个体时发生交叉互换的概率。</p></li>
<li><p><strong>swapping_prob</strong> -- 在交叉互换中交换父代参数中每一个参数的概率。</p></li>
<li><p><strong>seed</strong> -- 随机数生成器种子。</p></li>
</ul>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">注解</p>
<p>在 v1.5.0 中作为试验性特性引入，在未来版本中，该接口可能在没有预先告知的情况下被改变。参考 <a class="reference external" href="https://github.com/optuna/optuna/releases/tag/v1.5.0">https://github.com/optuna/optuna/releases/tag/v1.5.0</a>.</p>
</div>
</dd></dl>

<dl class="py class">
<dt id="optuna.multi_objective.samplers.RandomMultiObjectiveSampler">
<em class="property">class </em><code class="sig-prename descclassname">optuna.multi_objective.samplers.</code><code class="sig-name descname">RandomMultiObjectiveSampler</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">seed</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(在 Python v3.8)">int</a><span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/optuna/multi_objective/samplers/_random.html#RandomMultiObjectiveSampler"><span class="viewcode-link">[源代码]</span></a><a class="headerlink" href="#optuna.multi_objective.samplers.RandomMultiObjectiveSampler" title="永久链接至目标">¶</a></dt>
<dd><p>使用随机采样的 Multi-objective sampler</p>
<p>该 sampler 是基于 <em>independent sampling</em> 的。关于 'independent sampling' 的更多细节请参考 <a class="reference internal" href="#optuna.multi_objective.samplers.BaseMultiObjectiveSampler" title="optuna.multi_objective.samplers.BaseMultiObjectiveSampler"><code class="xref py py-class docutils literal notranslate"><span class="pre">BaseMultiObjectiveSampler</span></code></a> 。</p>
<p class="rubric">示例</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">optuna</span>
<span class="kn">from</span> <span class="nn">optuna.multi_objective.samplers</span> <span class="kn">import</span> <span class="n">RandomMultiObjectiveSampler</span>

<span class="k">def</span> <span class="nf">objective</span><span class="p">(</span><span class="n">trial</span><span class="p">):</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">trial</span><span class="o">.</span><span class="n">suggest_uniform</span><span class="p">(</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
    <span class="n">y</span> <span class="o">=</span> <span class="n">trial</span><span class="o">.</span><span class="n">suggest_uniform</span><span class="p">(</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">x</span> <span class="o">**</span> <span class="mi">2</span><span class="p">,</span> <span class="n">y</span> <span class="o">+</span> <span class="mi">10</span>

<span class="n">study</span> <span class="o">=</span> <span class="n">optuna</span><span class="o">.</span><span class="n">multi_objective</span><span class="o">.</span><span class="n">create_study</span><span class="p">(</span>
    <span class="p">[</span><span class="s2">&quot;minimize&quot;</span><span class="p">,</span> <span class="s2">&quot;minimize&quot;</span><span class="p">],</span>
    <span class="n">sampler</span><span class="o">=</span><span class="n">RandomMultiObjectiveSampler</span><span class="p">()</span>
<span class="p">)</span>
<span class="n">study</span><span class="o">.</span><span class="n">optimize</span><span class="p">(</span><span class="n">objective</span><span class="p">,</span> <span class="n">n_trials</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
</pre></div>
</div>
<dl class="simple">
<dt>Args:</dt><dd><p>seed: 随机数生成器种子。</p>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">注解</p>
<p>在 v1.4.0 中作为试验性特性引入，在未来版本中，该接口可能在没有预先告知的情况下被改变。参考 <a class="reference external" href="https://github.com/optuna/optuna/releases/tag/v1.4.0">https://github.com/optuna/optuna/releases/tag/v1.4.0</a>.</p>
</div>
</dd></dl>

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